Papers with Yelp reviews
Sentiment Analysis of Yelp Review Dataset: A Comparative Study of Machine Learning Methods (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for sentiment analysis are inconsistent and require manual processing. |
| Approach: | They use natural language processing and machine learning to classify Yelp reviews' sentiments. |
| Outcome: | The proposed model outperforms other models on Yelp reviews. |
Bootstrapping Polar-Opposite Emotion Dimensions from Online Reviews (L18-1)
Copied to clipboard
| Challenge: | Existing bootstrapping methods for learning lexicons from unannotated online texts have important drawbacks. |
| Approach: | They propose a bootstrapping approach that softly labels unlabeled terms for polar-opposite emotion dimension values from the Ortony/Clore/Collins model of emotions. |
| Outcome: | The proposed approach achieves considerably better performance than several baselines. |
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
Copied to clipboard
| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
| Approach: | They propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. |
| Outcome: | The proposed pipeline outperforms baseline models on Yelp reviews, Amazon reviews, and news headlines in terms of its Semantic Text Exchange Score (STES) |
Distributionally Robust Language Modeling (D19-1)
Copied to clipboard
| Challenge: | Language models are generally trained on data spanning a wide range of topics but might be applied to an unknown target distribution. |
| Approach: | They propose a distributionally robust optimization procedure which minimizes the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. |
| Outcome: | The proposed method reduces the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. |
Prompted Opinion Summarization with GPT-3.5 (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent years have seen several shifts in summarization research, including extractive models. |
| Approach: | They propose a pipeline method for applying GPT-3.5 to summarize user reviews . they propose three new metrics targeting faithfulness, factuality, and genericity . |
| Outcome: | The proposed methods perform well in opinion summarization, the authors show . they also show that standard evaluation metrics do not reflect this performance . |